Laboratory Evaluation of a Low-cost, Real-time, Aerosol Multi-sensor
Overview
Occupational Medicine
Affiliations
Exposure to occupational aerosols are a known hazard in many industry sectors and can be a risk factor for several respiratory diseases. In this study, a laboratory evaluation of low-cost aerosol sensors, the Dylos DC1700 and a modified Dylos known as the Utah Modified Dylos Sensor (UMDS), was performed to assess the sensors' efficiency in sampling respirable and inhalable dust at high concentrations, which are most common in occupational settings. Dust concentrations were measured in a low-speed wind tunnel with 3 UMDSs, collocated with an aerosol spectrometer (Grimm 1.109) and gravimetric respirable and inhalable samplers. A total of 10 tests consisting of 5 different concentrations and 2 test aerosols, Arizona road dust and aluminum oxide, were conducted. For the Arizona road dust, total particle count was strongly related between the spectrometer and the UMDS with a coefficient of determination (R) between 0.86-0.92. Particle count concentrations measured with the UMDS were converted to mass and also were related with gravimetrically collected inhalable and respirable dust. The UMDS small bin (i.e., all particles) compared to the inhalable sampler yielded an R of 0.86-0.92, and the large bin subtracted from the small bin (i.e., only the smallest particles) compared to the respirable sampler yielded an R of 0.93-0.997. Tests with the aluminum oxide demonstrated a substantially lower relationship across all comparisons. Furthermore, assessment of intra-instrument variability was consistent for all instruments, but inter-instrument variability indicated that each instrument requires its own calibration equation to yield accurate exposure estimates. Overall, it appears that the UMDS can be used as a low-cost tool to estimate respirable and inhalable concentrations found in many workplaces. Future studies will focus on deployment of a UMDS network in an occupational setting.
Peck A, Handy R, Sleeth D, Schaefer C, Zhang Y, Pahler L Toxics. 2023; 11(1).
PMID: 36668782 PMC: 9862639. DOI: 10.3390/toxics11010056.
Li K, Sward K, Deng H, Morrison J, Habre R, Franklin M Sci Rep. 2021; 11(1):24052.
PMID: 34912034 PMC: 8674322. DOI: 10.1038/s41598-021-03515-1.
W-TSS: A Wavelet-Based Algorithm for Discovering Time Series Shapelets.
Li K, Deng H, Morrison J, Habre R, Franklin M, Chiang Y Sensors (Basel). 2021; 21(17).
PMID: 34502692 PMC: 8434226. DOI: 10.3390/s21175801.
Indoor Air Quality Issues for Rocky Mountain West Tribes.
Webb L, Sleeth D, Handy R, Stenberg J, Schaefer C, Collingwood S Front Public Health. 2021; 9:606430.
PMID: 33748060 PMC: 7973111. DOI: 10.3389/fpubh.2021.606430.
Adane M, Alene G, Mereta S Environ Health Prev Med. 2021; 26(1):1.
PMID: 33397282 PMC: 7783973. DOI: 10.1186/s12199-020-00923-z.